Biometric identification by garments having a plurality of sensors

ABSTRACT

Biometric identification methods and apparatuses (including devices and systems) for uniquely identifying one an individual based on wearable garments including a plurality of sensors, including but not limited to sensors having multiple sensing modalities (e.g., movement, respiratory movements, heart rate, ECG, EEG, etc.).

CROSS REFERENCE TO RELATED APPLICATIONS

This patent application claims priority to U.S. provisional patentapplication No. 62/357,665, titled “BIOMETRIC IDENTIFICATION BY WORNMOVEMENT SENSORS,” and filed on Jul. 1, 2016, the entirety of which isherein incorporated by reference in its entirety.

INCORPORATION BY REFERENCE

All publications and patent applications mentioned in this specificationare herein incorporated by reference in their entirety to the sameextent as if each individual publication or patent application wasspecifically and individually indicated to be incorporated by reference.

FIELD

Described herein are systems and methods to determine and/or confirm theidentity of an individual based on an analysis of sensed parameters froma plurality of sensors worn as part of an integrated garment. Thesensors may include a plurality of sensor management subsystems (SMSes)distributed in characteristic positions as part of the garment(s). TheseSMSes may be coordinated for local sensing, including precisetime-coordination with a central processor, and may record a variety ofdifferent parameters including, but not limited to individuals bodymovements, including voluntary movements (e.g., gait, arm, hand, leg,finger, foot, knee, elbow, chest, etc. movements), and involuntarymovements or reactions (e.g., respiratory rate, heart rate, ECG, EMG,EOG, etc.), from which a biometric pattern may be determined. Thevoluntary and involuntary movements or reactions may be linked to thevoluntary movements. A biometric indicator may be learned by the systemwhile wearing the apparatus, and features extracted from the recordeddata in order to generate a biometric template. The biometric templatemay be stored and used as a test against future biometric templates(tokens) from the same or different garments worn by the user touniquely identify the user. Described herein are methods of forming anidentifying biometric template, methods of storing and transmitting thebiometric template information securely, and/or methods of using thebiometric template to uniquely and accurately identify an individual.Also described herein are the apparatuses (devices and systems)performing these methods as well.

For example, described herein are garments having a variety of sensorsforming SMSes that may be used to determine, confirm, or analysisbiometric identification.

BACKGROUND

It has become increasingly important to uniquely identify an individual.Stealing or hacking personal, financial, medical and securityinformation is increasingly common. Attacks against digital informationdatabases are increasing. For example, by 2015, fraudulent cardtransactions have exceeded $11 billion a year worldwide, of which theU.S. represents 50%, while Europe follows with 15% of the total. Healthinsurance providers are one of the many industries most affected byhacking. In 2014, 47% of American adults had their personal informationstolen by hackers-primarily through data breaches at large companies. In2013, 43% of companies had a data breach in which hackers got into theirsystems to steal information. Data breaches targeting consumerinformation are on the rise, increasing 62% from 2012 to 2013, with 594%more identities stolen. Data about more than 120 million people has beencompromised in more than 1,100 separate breaches at organizationshandling protected health data since 2009. The data reflects astaggering number of times individuals have been affected by breaches atorganizations trusted with sensitive health information.

Some of the data can be used to pursue traditional financial crimes,such as setting up fraudulent lines of credit, but it can also be usedfor medical insurance fraud, including purchasing medical equipment forresale or obtaining pricey medical care for another person. Personalinformation is also at risk, including information about an individual'smental health or HIV treatments.

Existing solutions are not adequate. For example, the security ofpasswords (e.g., password-protected systems) depends on a variety offactors. Compromising attacks, such as protection against computerviruses, man-in-the-middle attacks (where the attacker secretly intrudesinto the communication of two unaware parties intercepting theirconversation), physical breech (such as bystanders steeling the passwordby covertly observing thorough video cameras, e.g., at ATMs machines),etc. The stronger the password, the more secure is the information itprotects. Strength may be a function of length, complexity andunpredictability. Using strong passwords lowers overall risk of asecurity breach, but strong passwords do not replace the need for othereffective security controls. The effectiveness of a password of a givenstrength is strongly determined by the design and implementation of thefactors (knowledge, ownership, inherence).

Tokens (security tokens) are used to prove one's identityelectronically, as in the case of a customer trying to access their bankaccount. The token is used in addition to or in place of a password toprove that the customer is who they claim to be. The token acts like anelectronic key to access something.

The simplest vulnerability with any password container is theft or lossof the device. The chances of this happening, or happening unawares, canbe reduced with physical security measures such as locks, electronicleashes, or body sensors and alarms. Stolen tokens can be made uselessby using two factor authentication. Commonly, in order to authenticate,a personal identification number (PIN) must be entered along with theinformation provided by the token the same time as the output of thetoken.

Any system which allows users to authenticate via an untrusted network(such as the Internet) is vulnerable to man-in-the-middle attacks. Inthis type of attack, a fraudulent party acts as the “go-between” theuser and the legitimate system, soliciting the token output from thelegitimate user and then supplying it to the authentication systemthemselves. Since the token value is mathematically correct, theauthentication succeeds and the party is improperly granted access.

Trusted as much a regular hand-written signature, a digital signatureshould ideally be made with a private key known only to the personauthorized to make the signature. Tokens that allow secure on-boardgeneration and storage of private keys enable secure digital signatures,and can also be used for user authentication, as the private key alsoserves as a proof for the user's identity.

For tokens to identify the user, all tokens must have some kind ofnumber that is unique. Not all approaches fully qualify as digitalsignatures according to some national laws. Tokens with no on-boardkeyboard or another user interface cannot be used in some signingscenarios, such as confirming a bank transaction based on the bankaccount number that the funds are to be transferred to.

Biometrics (e.g., biometric identification systems) often physicalfeatures to check a person's identity, ensure much greater security thanpassword and number systems. Biometric features such as the face or afingerprint can be stored on a microchip in a credit card, for example.A single feature, however, sometimes fails to be exact enough foridentification. Another disadvantage of using only one feature is thatthe chosen feature is not always readable.

A template protection scheme with provable security and acceptablerecognition performance has thus far remained elusive. Development ofsuch a scheme is crucial as biometric systems are beginning toproliferate into the core physical and information infrastructure of oursociety. Described herein are methods and apparatuses that may addressthe issues discussed above.

SUMMARY OF THE DISCLOSURE

Described herein are apparatuses (systems, methods, including garments,etc.) and methods that allow individual owners to use their identifier,which may be based on a wearable (e.g., garment) capable ofmedical-level physiological data and biometrics measuring, acting as acommunication platform, which may allow a user to uniquely identifyherself/himself in order to perform security-sensitive actions such asbeing identified, generating medical data, transferring funds,purchasing goods, modify contracts, enter in restricted—access areas,etc., with certainty of identity, without divulging data to a thirdparty, minimizing the risk of data being stolen. These methods andapparatuses may convert data detected in a predefined manner from any ofthe wearable apparatuses described herein (or similar in at least someof the functional characterisitcs described herein) into biometrictemplate information that may be stored and later compared against othersimilarly-acquired biometric information to confirm a user's identity.This information may act as a token in a security protocol, method orsystem. These methods and apparatuses may generate the biometricinformation from one or more wearable garments including a plurality ofintegrated SMSes; the garment may securely receive, record and transmita biometic template or token derived from the one sensor (or more likelyplurality of sensors) integrated into the garment(s), in minimal timeand with minimal cost. A biometric may be a measurement of aphysiological trait, traditionally such as fingerprint, iris pattern,retina image, hand or face geometry, or it can be a behavioral traitsuch as voice, body sweating, gait. Current biometric technologyidentifies individuals automatically through one or several of thesetraits. Automatically means that the person's trait has been scanned,converted into a digital form in a database or on identity card. Thuscurrent technology obliges individuals to divulge their data (to thedatabase that will identify them) with the risk of the database beinghacked or the card being stolen. The moment users divulge their datathey have lost it, potentially irrevocably: unlike passwords, biometricscannot be easily changed. Furthermore current biometric technology maynot be accurate because it is not able to be universally present, uniqueto the individual, stable over time and easily measurable and have thedisadvantage that, unlike a password, a person's characteristics are notsecret and can therefore be copied. Once copied biometric data is lostforever: unlike a password it cannot be reset. The methods andapparatuses (e.g., systems and devices) described herein may overcomethese limitations. See, e.g., U.S. Pat. No. 6,016,476, describing aportable information and transaction processing system and methodutilizing biometric authorization and digital certificate security.

Commonly used biometric traits include fingerprint, face, iris, handgeometry, voice, palmprint, handwritten signatures, and gait. Biometrictraits have a number of desirable properties with respect to their useas an authentication token, namely, reliability, convenience,universality, and so forth. These characteristics have led to thewidespread deployment of biometric authentication systems. But there arestill some issues concerning the security of biometric recognitionsystems that need to be addressed in order to ensure the integrity andpublic acceptance of these systems. There are five 5 major components ina generic biometric authentication system, namely, 1) sensor, 2) featureextractor, 3) template database, 4) matcher, and 5) decision module. 1)Sensor is the interface between the user and the authentication systemand its function is to scan the biometric trait of the user. 2) Featureextraction module processes the scanned biometric data to extract thesalient information (feature set) that is useful in distinguishingbetween different users. In some cases, the feature extractor ispreceded by a 2A) quality assessment module which determines whether thescanned biometric trait is of sufficient quality for further processing.

The systems described herein may not need all of these components, sincebiometric data may are not necessarily stored in a database; insteadthese systems may use data generate during the biometric identificationprocess. Thus, these systems may not need a template database.Otherwise, during enrollment, the extracted feature set may be stored ina database as a template (XT) indexed by the user's identityinformation. Since the template database could be geographicallydistributed and contain millions of records (e.g., in a nationalidentification system), maintaining its security is often not a trivialtask. The matcher module is usually an executable program, which acceptstwo biometric feature sets XT and XQ (from template and query, resp.) asinputs, and outputs a match score (S) indicating the similarity betweenthe two sets. Finally, the 5) decision module makes the identitydecision and initiates a response to the query.

A fish-bone model can be used to summarize the various causes ofbiometric system vulnerability. At the highest level, the failure modesof a biometric system can be categorized into two classes: intrinsicfailure and failure due to an adversary attack. Intrinsic failures occurdue to inherent limitations in the 1) sensing, 2) feature extraction, or3) matching technologies as well as the 4) limited discriminability ofthe specific biometric trait. In adversary attacks, a resourceful hacker(or possibly an organized group) attempts to circumvent the biometricsystem for personal gains. We further classify the adversary attacksinto three types based on factors that enable an adversary to compromisethe system security. These factors include system administration,nonsecure infrastructure, and biometric overtness.

Intrinsic failure is the security lapse due to an incorrect decisionmade by the biometric system. A biometric verification system can maketwo types of errors in decision making, namely, 1) false accept and 2)false reject. A genuine (legitimate) user may be falsely rejected by thebiometric system due to the large differences in the user's storedtemplate and query biometric feature sets. These intra-user variationsmay be due to incorrect interaction by the user with the biometricsystem (e.g., changes in pose and expression in a face image) or due tothe noise introduced at the sensor (e.g., residual prints left on afingerprint sensor). False accepts are usually caused by lack ofindividuality or uniqueness in the biometric trait which can lead tolarge similarity between feature sets of different users (e.g.,similarity in the face images of twins or siblings). Both intrauservariations and interuser similarity may also be caused by the use ofnonsalient features and nonrobust matchers. Sometimes, a sensor may failto acquire the biometric trait of a user due to limits of the sensingtechnology or adverse environmental conditions. For example, afingerprint sensor may not be able to capture a good quality fingerprintof dry/wet fingers. This leads to failure-to-enroll (FTE) orfailure-to-acquire (FTA) errors. Intrinsic failures can occur even whenthere is no explicit effort by an adversary to circumvent the system. Sothis type of failure is also known as zero-effort attack. It poses aserious threat if the false accept and false reject probabilities arehigh. Ongoing research is directed at reducing the probability ofintrinsic failure, mainly through the design of new sensors that canacquire the biometric traits of an individual in a more reliable,convenient, and secure manner, the development of invariantrepresentation schemes and robust and efficient matching algorithms, anduse of multibiometric systems

The apparatuses and methods described herein may allow one to build ameasuring systems that can reduce or eliminate the risk of incorrectdecisions being made by the biometric system by synthesizing a largevariety (e.g., large array) of biometic data (e.g., specifying which,why and how) provided by the apparatus/garment acting as a biometricsystem and/or communications platform.

The methods and apparatuses described herein may provide biometricsecurity that may possess the following four properties. Diversity: thesecure template must not allow cross-matching across databases, therebyensuring the user's privacy. Revocability: it should be straightforwardto revoke a compromised template and reissue a new one based on the samebiometric data. Security: it must be computationally hard to obtain theoriginal biometric template from the secure template. This propertyprevents an adversary from creating a physical spoof of the biometrictrait from a stolen template. Performance: the biometric templateprotection scheme should not degrade the recognition performance (FARand FRR) of the biometric system.

Typically, biometric recognition is probabilistic; it is not anabsolutely accurate and certain identification technology and, accordingto critics, this is one of the technology's key limitations. In otherwords, biometric systems will always only provide a probability ofverification. There have been moves to manage the probabilistic natureof biometric matching and the challenges that this represents, forexample by introducing ‘multi-modal biometrics’ such that the uniquenessof a match (i.e. the likelihood of making a correct match) increaseswith the number of biometrics that are combined (i.e. whilst it islikely that someone might have a fingerprint pattern that matches yours,it is far less likely that someone will have both a fingerprint and aniris image which match yours). In other words: the fusion of multiplebiometrics helps to minimise the system error rates.

However, the use of multi-modal biometric systems then entails adifferent set of limitations and challenges. First, multi-modalbiometrics is more expensive as it requires more data to be collectedand processed. Besides that, another challenge confronting theimplementation of multi-modal biometric systems is that a crucialquestion still remains unresolved; namely the question of what are thebest combinations (modalities). Moreover, multi-modal biometric systemsare also challenging to implement because of the complexities involvedin making decisions “about the processing architecture to be employed indesigning the multi-modal biometric system as it depends upon theapplication and the choice of the source. Processing is generallycomplex in terms of memory and or computations.” Besides that, there arealso still a number of unresolved issues about the scalability ofmulti-modal biometric systems. Finally, increasing the amount ofbiometrics being collected from an individual might increase theperformance of the system but might also, at the same time, increase therisk of data theft or misuse of individual information.

Biometrics can be defined as “any automatically measurable, robust anddistinctive physical characteristic or personal trait that can be usedto identify an individual or verify the claimed identity of anindividual.” Contemporary biometric technologies may entail thedigitalization of the unique body part, a process that has implicationsfor the knowledge produced from the processing of this digitalizedbiometric data and hence for the body subjected to this technology, inparticular given the possible political use of suchbiometrically-derived knowledge.

Described herein are systems that through a wearable apparatus (e.g., awearable computing & communicating device that covers a significant partof the user's body, e.g., one o more of: torso, arms, legs; and may alsoinclude one or more of: head, hands, feet, etc.) accurately measures aplurality of biometrical data (using the same or multiple modalities) togenerate an accurate identification of a person, in a private (no thirdparty intrusion), automatic (directly executed by the computing andcommunication module thus sidestepping user intervention that couldgenerate errors), simple (identification is activated by a single inputsuch as a voice command, a touch on the garment, such as a touch point,a smart screen touch, etc.), fast (synthesis can be produced in just afew seconds), repeatable (it can be generated as many times as needed),low cost (e.g., virtually no execution cost to the owner of apparatus)and controlled manner (the user is in control and needs no externalsupport). The apparatus may generate its owner identity: a synthesis oftraits and data that make her/him unmistakably who she/he is. Mostimportantly, the system allows the person to be the sole owner of theidentification data produced. Present biometric-recognition systemsrequire sharing data with a database owned by a third party (government,medical facility, financial institution, vendor, etc.) in order for theperson to be identified. Being identified through biometrics today has asubstantial cost to the data owners of data: they lose ownership oftheir data and possibility to generate and income with it. In today evermore digital economy, data is becoming exponentially more valuable: thevalue is today collected by large corporations rather than by theirnatural/legitimate owners, partly a cause of today vast economic divide.Securing ownership of personal data could be a mean to close the dividegap by allowing owners to monetize their ever more valuable data.

The biometric identification apparatuses described herein may be:universal, i.e., each individual possesses this characteristic; easilymeasured, i.e., it is quite easy technically and convenient for anindividual to obtain the characteristic; unique, i.e., there are no twoindividuals with identical characteristics; and permanent, i.e., thecharacteristic does not change over time.

Ideally the characteristic should be universally present, unique to theindividual, stable over time and easily measurable. No biometriccharacteristics have been formally proven to be unique, although theyare usually sufficiently distinct for practical uses. Differentbiometrics will be more suitable for different applications depending,for example, on whether the aim is to identify someone with theirco-operation or from a distance without their knowledge.

For example, described herein are methods of confirming a user'sidentity using a garment including a variety of sensors. For example,the method may include: wearing a garment comprising a plurality ofintegrated sensors at predetermined locations; synchronously recordingsensor data from multiple predetermined locations on the garment;generating, in the garment, a biometric token from the recorded sensordata; transmitting the biometric token to a lodger in or on the garment;and transmitting the biometric token to a third party having a biometrictemplate against which the biometric token may be tested.

Generating a biometric token from the recorded sensor data may comprisegenerating the biometric token in a master and/or scheduler on thegarment. The master and/or scheduler may include a processor.

Wearing may include adjusting the position of the sensors based onhaptic feedback from the garment. For example, the garment may includeone or more haptics that will vibrate or otherwise indicate that anearby sensor in the garment is not properly positioned on the user'sbody.

Synchronously recording sensor data may comprise synchronously recordingsensor data from a plurality of motion sensors. The sensors may be ofdifferent types (e.g., different modes, such as respiration sensors,cardiac sensors, galvanic skin sensors, EMG sensors, EEG sensors, etc.).Synchronously recording sensor data may comprise synchronously recordingsensor data from a plurality of motion sensors, one or more respirationsensors and one or more electrodes configured to contact the user's skinwhen the garment is worn. Wearing the garment may comprise wearing thegarment over the user's torso (e.g., the garment may be a shirt, or mayinclude a shirt). Synchronously recording may include synchronouslyrecording sensor data from multiple sensor types on the garment. Forexample, the scheduler and/or master may coordinate the recording ofsensor (slave) data; each sensor or sub-sets of sensors may record atdifferent frequencies based on the type of sensor it is. Thus,synchronously recording sensor data may comprise recording data at aplurality of frequencies.

Any of these methods may also include encrypting the biometric tokenprior to transmitting the biometric token to the third party. Thus, ingeneral, the biometric token is determined using the master and/orscheduler, which may also encrypt the biometric token.

Since the user may wear the apparatus (garment with sensors)continuously for a long period of time, the biometric token may bedetermined on an ongoing basis (e.g., a running window) and/or upondemand (e.g., upon a request for identity verification).

Any of the methods and apparatuses described herein may also includeencrypting and transmitting the biometric template that can be used by athird party to compare with the biometric token. For example, thegarments described herein may generate a biometric template upon sometriggering event 9 e.g, wearing the garment for a predetermined time) orupon request from a third party.

The substance of the biometric template and/or biometric token,including the type of data (sensor type, etc.) may be determined, forexample, based on the ability of that type of data to distinguishidentity of the individual wearing the garment. For example, thebiometric template may be constructed from accelerometer data (includingfrom one of the axes of motion of the accelerometer, such as one axis ofmotion of the accelerometer) and/or recorded electrical activity (e.g.,cardiac data, EMG data, galvanic skin response data, etc.) and/orrespiration data.

Any of these methods may also include sending a coded message requestingapproval of the wearer to proceed from the third party. An approvalmessage may be transmitted to the user in a coded (e.g., in a Morse-liketactile code), and a response code may be transmitted by responding tospecific location on the garment (e.g., tactile output) and/or to atouchscreen in communication with the device. Thus, contacting an outputon the garment may be used to indicate agreement to the third party.

For example, a method of confirming a user's identity may include:wearing a garment comprising a plurality of integrated sensors atpredetermined locations in the garment that are configured to positionthe integrated sensors over the user's torso; synchronously recordingsensor data from multiple predetermined locations on the garment, usinga plurality of different sensor types; generating, in the garment, abiometric token from the recorded sensor data; and transmitting thebiometric token to a third party having a biometric template againstwhich the biometric token may be tested.

A method of confirming a user's identity may include: wearing a garmentcomprising a plurality of integrated sensors at predetermined locationsin the garment that are configured to position the integrated sensorsover the user's torso; adjusting the position of the sensors usinghaptic feedback from the garment; synchronously recording sensor datafrom multiple predetermined locations on the garment, using a pluralityof different sensor types; generating, in the garment, a biometric tokenfrom the recorded sensor data; encrypting the biometric token; andtransmitting the encrypted biometric token to a third party having abiometric template against which the biometric token may be tested.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe claims that follow. A better understanding of the features andadvantages of the present invention will be obtained by reference to thefollowing detailed description that sets forth illustrative embodiments,in which the principles of the invention are utilized, and theaccompanying drawings of which:

FIG. 1 is a schematic illustrating one example of a method of using agarment having a plurality of sensors to generate a unique biometriccode (e.g., token or template).

FIG. 2 is an example of an apparatus (e.g., system) comprising a garmentfor measuring a biometric token or template, configured for medicalmonitoring.

FIGS. 3A-3C illustrate another example of a garment for determining abiometric token or template, configured as a performance/fitnessgarment.

FIGS. 4A-4B illustrate another example of a garment for determining abiometric token or template.

FIG. 5 is an example of a schematic for a general apparatus (e.g.,system) for determining biometric template/token information.

FIG. 6 is an example of a garment 600 including IMU units integrating a3D-accelerometer, a 3D-gyroscope and a 3D-magnetometer, ECG sensors,breathing sensors, skin-conductance and temperature sensors. Thisgarment may be further configured to determine a biometric template ortoken based on this sensor information.

FIGS. 7A-7C illustrate data from a prototypes (such as the one shown inFIG. 6) used for characterizing the behavior of a user can be identifiedwhether by a semi-supervised approach or in a completely unsupervisedway.

FIGS. 8A-8C illustrate the results of a Support Vector Data Description(SVDD) approach, that relies on the construction of a multidimensionaldomain around typical data points of the target user to identifybiometric data upon which to base (at least in part) a biometrictemplate or token.

FIGS. 9A-9C are similar to FIGS. 8A-8C, but illustrate a method ofapproach using the ‘worst’ feature.

FIGS. 10A-10F illustrate detection confidence for three users in asparse dataset.

FIGS. 11A-11F illustrate detection confidence for three users in asparse dataset in an alternative embodiment.

FIG. 12 is a generic biometric data system as described herein.

DETAILED DESCRIPTION

Described herein are biometric identification methods and apparatuses(including devices and systems) for uniquely identifying one anindividual based on a garment including one (or more preferably aplurality) of sensors, including but not limited to sensors havingmultiple sensing modalities (e.g., movement, respiratory movements,heart rate, ECG, EEG, etc.).

FIG. 1A illustrates an exemplary sequence of operations to produce theidentity synthesis. This sequence may be part of a method (or in anapparatus as software, hardware and/or firmware configured to controlthe apparatus to generate a biometric token or template that mayuniquely identify a user with a very high degree of certitude.

In the first step 101, the user (also referred to as a subject orwearer) may wear the device. In general, the device may be a garmentincluding a plurality of SMSes that each receive and/or record, and/orprocess sensor data from one or more sensors. For example, the apparatusmay be a garment such as the garments described in one or more of U.S.patent application Ser. No. 14/023,830, titled “WEARABLE COMMUNICATIONPLATFORM” (Now U.S. Pat. No. 9,282,893); U.S. patent application Ser.No. 14/331,142, titled “COMPRESSION GARMETS HAVING STRETCHABLE ANDCONDUCTIVE INK” (Now U.S. Pat. No. 8,948,839); U.S. patent applicationSer. No. 14/612,060, titled “GARMENTS HAVING STRETCHABLE AND CONDUCTIVEINK” (US-2015-0143601-A1); U.S. patent application Ser. No. 14/331,185,titled “METHODS OF MAKING GARMENTS HAVING STRETCHABLE AND CONDUCTIVEINK” (Now U.S. Pat. No. 8,945,328; U.S. patent application Ser. No.15/324,152, titled “GARMENTS HAVING STRETCHABLE AND CONDUCTIVE INK”;U.S. patent application Ser. No. 15/202,833, titled “SYSTEMS AND METHODSTO AUTOMATICALLY DETERMINE GARMENT FIT” (US-2016-0314576-A1); U.S.patent application Ser. No. 14/644,180, titled “PHYSIOLOGICAL MONITORINGGARMENTS” (US-2015-0250420-A1); U.S. patent application Ser. No.15/516,138, titled “DEVICES AND METHODS FOR USE WITH PHYSIOLOGICALMONITORING GARMENTS”; and U.S. patent application Ser. No. 15/335,403,titled “CALIBRATION PACKAGING APPARATUSES FOR PHYSIOLOGICAL MONITORINGGARMENTS,” each of which is herein incorporated by reference in itsentirety.

These apparatuses (e.g., garments) may include a tutorial application toensure that the device is properly worn and a) all the sensors areproperly functioning and/or correctly positioned. Alternatively or inaddition, when wearing the garment, the processor (e.g., computer)communicating with or integrated into the apparatus may detect that asensor is not working and may indicate it on the smartscreen (e.g.,touchscreen), and/or by haptic feedback near the sensor 103. Forexample, a message indicating that the sensor needs to be positioned orworn correctly/adjusted may appear on the smart phone or computer'sscreen in communication with or integrated into the garment.

In general, sensors integrated into the garment(s) may be properlypositioned in the right place. For example: IMU need to be positioned inthe middle of the segment (shoulder to elbow, elbow to wrist), on theback of the hand between wrist and knuckles.

Once worn and adjusted, the device may be worn for a few minutes orlonger so that sensors adapt to body temperature.

The apparatus may then activate the production of synthesis of biometricdata from the plurality of sensors (e.g., from the plurality of SMSes).For example, the apparatus may be activated automatically or manually,e.g., through a touch point (touching a microchip on the sleeve forexample), through voice command, a sensorial command or other type ofcommand. Thereafter, the apparatus may produce a biometricrepresentation (e.g., token or template) of the wearer's physiologicaldata 107. This is described in greater detail below, and generallyincludes collecting sensor data, e.g., from coordinated SMSes on/in thegarment and analyzing the data in an ongoing or discrete manner toevaluate one or more characteristics (“prototypes”) specific to eachsensor (per characteristic sensor type and location). The biometricrepresentation may be perfected through machine learning. Thus, the morethe owner uses the device, the more precise the identity synthesisalgorithm becomes.

The method and biometric representation can also be made more accurateby using more than one garment or a garment covering more than oneregion. For example, the garment may be a garment configured to collectmedical diagnostic information. the wearer may wear the garment thatcovers the body from the tip of the toes (leggings incorporating socks)to the top of the head/balaclava see, e.g., FIG. 2.

The apparatus in FIG. 2 is an exemplary system that includes abodysuit/garment 1 a headpiece 2, an optional pulse oximetersub-subsystem 3, a controller (e.g., phone module) 4, an optionalbattery pack 5, a touchscreen display 6, a remote server (e.g., cloud)7, and automatic analysis software 8, which may execute on the remoteserver and/or on the controller. This apparatus can provide many hoursof a very large array of physiological data recording through a longperiod of time (from a few hours to 100 hours plus with auxiliarybatteries). This exemplary apparatus may be used from 12 to 48 hours(e.g., while sleeping and in daily activity) once a week or once amonth.

The system shown in FIG. 2 may monitor, for example, respiratorymechanics, PSG, e.g.,: thoracic and abdominal movements, sleep patterns,oxygen saturation (including the time course of oxygen saturation indifferent body regions under different activity conditions), ECGmeasurements (e.g., via an integrated Holter 12 lead ECG sensors). Anyof these garments may also include a plurality of movement sensors, suchas accelerometers at predetermined positions on the body, secured inreproducible relation to the body by the garment.

Other garments covering more or less of the body may be used. Forexample, a garment configured as an efficiency device that may monitorand provide feedback to the owner during daily life to improve healthby, for example, analyzing activities and improving habits, may also beused. This apparatus may be, for example, an upper-body device withshort or long sleeves very comfortable to be worn during daily life andmay optionally include a visor or glasses to monitor EEG, EOG, EMGfacial signals, body temperature, and one or more IMUs to monitor headmovements, etc. See, e.g., FIGS. 3A-3C. FIG. 3A shows another variationof a wearable sensing garment having a plurality of sensors 309 on thefront 301 and back 303 of the garment. The garment may be worn with atouchscreen 305 at or near the wrist/forearm of the wearer. A collarunit 307 may include a speaker and one or more microphones (e.g., forvoice recognition, etc.). The variation show in FIG. 3A is ashort-sleeved garment. A similar long-sleeved variation is shown in FIG.3B. Additional (and optional) accessory such as headband/neckband 315,smartphone 317 and battery pack 319 are shown in FIG. 3C. The sensorsshown may include electrodes for measuring galvanic skin responses,movement (e.g., 9 or more IMUs), electrodes for measuringelectrocardiograms (ECGs), electrodes for measuring EMGs, and groundelectrode(s).

Other garments may also include an apparatus configured as a performancedevice that supports the owner during regular or intensive fitnessactivities or professional sports. See, e.g., FIGS. 4A (front 301) and4B (back 303) of an exemplary garment. In this example, the garment alsoincludes a plurality of sensors 409 (e.g., galvanic skin responses,movement (e.g., 9 or more IMUs), electrodes for measuringelectrocardiograms (ECGs), electrodes for measuring EMGs, and groundelectrodes, etc.). The garment may also include a collar 405, 405′ andspeakers (shown as earpieces 411). The optional components shown in FIG.3C may also be used with the garment of FIG. 4A-4B.

By wearing any of these garments for a period of time (e.g., 1 day, 1week, 2 or more weeks, 1 month, or more months, etc.) for short periodof time (e.g., with the medical device garment of FIG. 2, e.g., once aweek, with the garment of FIG. 3A-3C, every day for a few hours, withthe performance/fitness garment, 2 to 3 times a week), the apparatus maydevelop a knowledge of the heart at a medical diagnostic ECG level evenwhen using the apparatus despite the fact that it only has, e.g., 2sensors rather than the 12 derivations.

Physiological data captured by the many sensors may be processed inmultiple locations throughout the body. For example, the sensors (e.g.,IMUs or EMGs) may be positioned in proximity of an SMS (e.g., microchip)that process the data. The physiological data may be jointly processedinto the Sensor Management System (SMS). Thus, the data may besynchronously processed at multiple locations in the garment 105; thedifferent processors may be synchronized and the data accurately timestamped (e.g., to within +/−1 ms, 0.1 ms, 0.001 ms, etc.). Thesynchronized data are processed/calculated with minimal latency, and maybe recombined and/or further processed. SMS software and/or firmware cancalculate data at different Hertz velocities depending on the type ofphysiological data. For example IMU may be measured at 500 Hertz, heartrate at the same or at a different frequency (e.g., 100 Hz or less),respiration at the same or at a different frequency (e.g., 10 Hz), EEGat the same or at a different frequency (e.g., 200 Hz), EOG at the sameor at a different frequency (e.g., 300 Hz), EMG at the same or at adifferent frequency, Skin conductance at the same or at a differentfrequency, body temperature at the same or at a different frequency,etc.

In general, any of the methods and apparatuses described herein mayinclude tactile feedback, via one or more haptic actuators (e.g.,piezoelectric actuators, etc.). For example, the devices may be equippedwith haptic actuators to provide touch feedback at or near thesensor(s). Haptic feedback may be provided when confirming that thesensor(s) are correctly positioned. Haptic actuators may provide atactile feedback to the user to indicate that the synthesis has beenperformed by the SMS. The synthesis may include the formation of abiometric template or token that is synthesized from a plurality ofdifferent sensors or combination of sensors in/on the garment. Oncesynthetized, the biometric template or token may be encrypted. Forexample, the synthesis of the biometric template/token may be anencrypted 532 to 1064 characters in SMS.

The synthesized biometric template or token may then be sent by a lodger109 (a telecommunications module, such as a cell phone orwireless-enabled unit that may be located in or on the garment, e.g., onthe upper-back between the shoulder blades in a torso garment such as ashirt). The biometric template or token may be sent to an interestedparty 111 that may verify the biometric token and then send a codedmessage requesting approval of the wearer to proceed, assuming that thebiometrics match 113. The request for approval may be displayed on thegarment, including on a display integrated into or in communication withthe garment. Approval may be provided by a touchpoint in/on the garmentand/or a touchscreen. For example, in case of a bank access, beforeapproval of a payment, the biometric information may be transmitted fromthe garment (lodger) to the bank, acting as the third party. Assumingthat the bank has a reference biometric template to compare to (which isalso encoded), the bank may verify the biometric information from thegarment and may then request additional verification. Additional(optional) security may then be provided; for example, the coded messagemay be delivered on the garment by haptic actuators in a Morse-type codechosen by the user. The user may then send approval to the bank. In somevariations, the synthesis can be stored in a blockchain.

In general, the garments described herein may include a sensor network(e.g., a network of sensor elements, including a master, a scheduler,and one or more slaves (sensors). The slave(s) may be the lastelement(s) of the sensor network, and may typically be placed directlyon the garment. More than one slave sensor can be attached to the sensornetwork. As mentioned, the garment may support more than one sensor. Theslaves/sensors may be responsible to: directly acquire data fromsensors, execute signal processing, execute algorithms, derive virtualsensor data from hardware sensors (e.g., Quaternions), etc.

Different sensor types supported. For example, slave breath sensors(e.g., “Type ECG-BREATH”) may be configured to acquire data from a12-lead ECG and breathing sensors. Slave motion sensors (e.g., “TypeIMU-EMG”) may be configured to acquire data from an IMU (e.g.,Accelerometer, Gyroscope, Magnetometer, Quaternions) and/or EMG sensors.

A scheduler may be placed inside of a control device or directly on/in agarment. The scheduler may generally manage the sensor network of thegarment, and may organize slaves to execute synchronous sampling. Thescheduler may control and synchronize the clocks in the individualregions of the garment (and may include a master clock, and maycoordinate the sample frequencies and/or synchronize the sensors). Thescheduler may also encrypt data provided to the master, and/or providethe access of the sensor network to the master. The scheduler mayinclude circuitry (e.g., clock, processor, memory, etc.).

A master may also be included in the control device, and may beconfigured to manage the sensor network (e.g., thorough the scheduler).The master may obtain data from the sensor network (e.g., encrypted bythe scheduler), and may execute control logic (e.g. processes) and/ormay directly acquire data from the sensors, store data, exchange datawith a remote server (e.g., the cloud, for example, through WiFi/mobilenetwork), exchange data with an external user device (e.g., throughWiFi/Bluetooth), and/or exchange data with an external third partymedical devices (e.g., through Bluetooth).

FIG. 5 is a schematic overview of an apparatus (configured as a systemin this example) as described. In FIG. 5, the master 501 communicatesdirectly with the scheduler 503, while the scheduler communicates withthe plurality of sensors (slave 505, 505′, 505″, 505′″, etc.) in thegarment through a bus 507.

In some variations, the biometric apparatuses described herein arewearable devices that cover the major part of the body to maximize thenumber of sensors located around the body; in general, the higher thenumber of sensor the higher the medical accuracy of the data. This mayalso help to ensure that sensors are located in the best possible partof the body for maximum precision. A sensor located around the heart maybe more precise then a sensor on the wrist (like in wearable braceletsand watches). The device may be comfortable (e.g., preventing data noisedistortions introduced by constriction/lack of comfort), and can be usedduring daily life (generating more relevant data and habits far from theanxieties and risks of hospitals and medical laboratories) for longperiod of time. Longer measurement times may enhance the chance todiscover pathologies or abnormalities in garments configured for medicaluse, and may also provide greater accuracy for the data through machinelearning.

The apparatuses described herein may not need a password to authenticatean individual, which may substantially increasing the ease of use.Passwords may get misplaced or are forgotten. The biometric technologieslinked to the particular individual such as those described herein mayprovide greater security, speed, and ease of use than traditionalmethods like passwords, PIN's, or “smart” cards. Biometric login canalso save time and reduce costs.

Rather than simply generate physiological data to compare to previouslystored physiological data bases, the methods and apparatuses describedherein may determine reliable biometric templates from sensors in/or agarment, these biometric templates may be generalizable betweendifferent garments. This may reduce the risk of the user's physiologicaldata being held in possession of a third party (e.g., such as the USgovernment as currently done for fingerprints and retinal scans). Thesystems, devices and methods described herein may help ensure that thepersons generating the physiological data remains the sole owner oftheir data and does not need to divulge their data in order to beidentified or in order to use their data to make transactions or tomonetize it.

Thus, in general, the validation server does not store sensitive userdata such as personally identifiable information (PII). A user's uniquebiometric signature may remain within trusted execution and may not everbe transmitted over the web. Raw biometric data may never be sentthrough the network or stored in a central database.

The systems described herein may replaces and compete with existingtokens. These systems are typically a synthesis of users' physiologicaldata. The methods and apparatuses described do not reveal the owner'sphysiological data, but merely provide extracted and/or calibratedinformation that may be further processed.

Advantageously, the use of multiple, synchronized sensors as describedherein may allow for rapid and robust sensing. For example, theapparatuses described herein may generate an accurate biometric tokenwithin under about 10 seconds. Typically these systems may only workswith the owner of the system. Once the system is worn for more than afew times (e.g., more than 5 times, more than 6 time, more than 7 times,more than 8 times, more than 9 times, more than 10 times, etc.) it mayrecognizes its owner and may be configured to only works when it is wornby the owner.

As mentioned, any combination of different physiological data types maybe used. for example, at least 3 types of physiological data (e.g., atleast four types, at least five times, at least six types, etc.) may beused to generate an accurate synthesis of the biometric template/token.For example, heart, respiration, movement, and rest (EEG, EOG, EMG,temperature, skin conductance, etc.), or any component part of these.For example, an accelerometer may include three different axes (x, y,z), which may be analyzed separately or together.

In any of these variations, SMS information may be encrypted so thatdata is protected before being sent. The data may be encrypted beforebeing passed into the phone module to guarantee safety. Once atransaction is automatically approved by a third party device aftercomparing the biometric template based on a wearable garment withsensors stored by the third party with a biometric token based on awearable garment with sensors, a message may be sent to the wearablegarment's haptic system of the wearer/owner of physiological data. Thehaptic communication may be a ‘pass-haptic signals’ in a Morse-type coderather than a ‘password’ and thus it can be reset.

The signal may be performed by two different haptic actuators placed intwo different parts of the body, which may oblige the owner to wear thedevice properly.

The data may be saved in a physiological data platform (e.g., in thecloud or in a secure remote server. The authentication may be given bythe physiological data platform after matching the data. A biometricencryption may help ensure that a user's credentials are decentralizedand stored offline. A cryptographic digital key may be generated from abiometric such as a fingerprint or voice and used to sign transactionsinitiated by a relying party. Raw biometric data may not be sent throughthe network or stored in a central database.

Thus, the authentication solutions described herein may providebiometric encryption without requiring an authentication channel relyingon a centralized storage of biometrics. End-users may be able to choosewhich biometric authenticators they will utilize. Biometric data mayremain encrypted and protected against malware on a user's device.Relying parties set policies for which biometric authenticators can beused. A UAF Server may provide the server side of UAF protocols; HYPRmakes it easy to deploy any FIDO server on-premises or as a cloudsolution.

Using public key cryptography, it is possible to prove possession of aprivate key without revealing that key. The authentication server mayencrypt a challenge (typically a random number, or at least data withsome random parts) with a public key; the device describe describedherein may allow the apparatus to prove it possesses a copy of thematching private key by providing the decrypted challenge.

The identification systems described herein may use a classical schemeincluding data acquisition, data preprocessing, formation of inputfeature space, transition to reduced feature space, and sensorinformation classification. The generic system structure (FIG. 12, left)shows the sequence of essential data processing stages. Feed forwardlinks show processed data transfer between stages. The output of onestage is the input to the subsequent stage. Each stage can beimplemented using different processing methods. The detailed systemstructure (FIG. 12, right) shows methods considered in this study foreach system stage. For most stages, these methods are alternatives, butthe data preprocessing stage is usually comprised of severalcomplementary methods.

EXAMPLES

Previously described biometric authentication has typically been basedon data derived from direct measurements of a part of the human body,like the DNA, fingerprint, retina, iris, face, ear, palm, the veins'pattern in the hand or in the wrist, etc. The heart activity has alsobeen used for the person authentication, whether by capturing theelectrical activity (ECG) or the sound produced by it (PCG).Photoplethysmography (PPG) has also been used for authentication. Veinpatterns have also been used. In addition, it is also possible toperform biometric authentication based on behavioral characteristics ofthe user, which may be linked/coordinated by these physiologicalresponses. For instance, gait, the way the user walks, signature andvoice recognition, keystroke-based or by capturing the response of theuser (e.g., EEG) to a given stimulus.

Typically, the raw signals captured from direct measurements of the userto be authenticated are characterized and authentication may be based ona comparison between the features of those measurements and the featuresof the signals measured on the candidate person. For instance,fingerprint authentication may be based on three basic patterns offingerprint ridges: arches, loops, and whorls. The features or datapoints defining the authenticated user can define a region or a set ofregions in a high-dimensional space. In this case, the procedure ofauthentication consists on computing if the candidate data lies insidethose regions.

Described herein are garments that may provide sufficient biometricinformation (on both voluntary and involuntary responses) to accuratelyand reliably be used as biometric identifying data; these garments mayfurther be configured to securely determine from the biometricinformation a synthesis of biometric templates or tokens that may beused to verify identity of an individual wearing the garment.

FIG. 6 is an example of a garment 600 including IMU units integrating a3D-accelerometer, a 3D-gyroscope and a 3D-magnetometer, ECG sensors,breathing sensors, skin-conductance and temperature sensors. The garmentin FIG. 6 illustrates one possible positioning of these sensors.

In a proof of concept test, multiple IMU units present in the samplegarment of FIG. 6 were examined for authentication. In particular, weused the accelerometer. In initial test, the accelerometer data was morereliable than the gyroscope data and the magnetometer was somewhatsusceptible to interferences from the environment and dependent on theorientation of the user. In practice, any or all of these sensors may beused. For example, the heart rate signal was, in preliminary data,somewhat noisy; however, the possibility remains for using the breathpattern and the exploitation of multiple modalities.

Initial tests identified sets of signal patterns that are uniquelypresent in a given individual. The resulting authentication system wouldbe of a behavioral type, given that those signals are generated, forinstance while the user is walking and working.

In a first approach, we exhaustively extracted all 1-second time-seriesof each axis of the available sensors (i.e., 5 sensors×3 axes ofacceleration, thus 15 axes). We then proceeded to group thosetime-series such that for many similar time-series patterns, we choseone single prototype (e.g., by means of a time-series clusteringtechnique such as K-medoids). The user's behavior is thus characterizedby a set of prototypes for each sensor axis (in our experiments, 15sensor axes×50 time-series prototypes). Those 750 prototypes may bedifferent for every user, or that at least, we can base theauthentication of a user on the distance between the measuredtime-series patterns of the candidate user and the prototype time-seriescharacterizing the authenticated user. Thus, the candidate user may berecognized as the authenticated user, if the aforementioned distance isbelow a certain threshold. The set of prototypes used for characterizingthe behavior of a user can be identified whether by a semi-supervisedapproach or in a completely unsupervised way. Results of this approachare summarized in FIGS. 7A-7C.

In a second approach, we analyzed if a user's way of behaving had aparticular pattern in the frequency domain, captured by the IMU'saccelerometers. We considered for this purpose all of the availableaccelerometers in all of the walking datasets. We then computed thepower spectrum of the signal for each accelerometer in each of the 3axes and kept the median signal over periods of 1 minute. We chose aresolution of 0.25 Hz in the frequency domain ranging from 0 to 20 Hz.These median spectra were used to construct a baseline (or prototype)for each specific user. We considered for this purpose a method calledSupport Vector Data Description (SVDD). This method relies on theconstruction of a multidimensional domain around typical data points ofthe target user. The domain is created using a recorded dataset and canthen be used to classify new measurements as belonging to the targetuser or not. Data points falling within the boundaries of the domain areconsidered as belonging to the user and points falling outside areconsidered outliers. Therefore, by counting the proportion of pointsthat fall in the domain with respect to the total number ofmeasurements, we can estimate quantitatively the likelihood of thegarment being worn by a specific user. Results are presented in FIGS.8A-8C and 9A-9C.

A first approach was to look at time-series clustering. The three plotsin FIGS. 7A-7C show the distances between the prototypes of three of theusers and the rest of users. For the sake of exemplification considerFIG. 7A. This plot shows the resulting distances when the sequencescoming from user COCO wearing the garment 108 were used for building thecodebook of prototypes. Hence, the blue curve represents the distancesbetween the prototypes of user COCO-108 and the sequences from the sameuser. Points before time=0 correspond to training observations. The restof the curves are the distances between the prototypes of user COCO-108and the sequences coming from other users (see the labels in the plot).We can say that the first approach effectively discriminate users inthis particular setup since the distances represented by the bottom 703curve (authenticated user) are lower than the distances represented bythe other curves (not authenticated users). The same analysis appliesfor the second and third row (user EDPI with garment 109 and user FRCAwith garment 115).

Moreover, we have tested how the distances changed depending on whichsensors are used. On the one hand, FIGS. 7A-7C show the resultingdistances when all the sensors axis are used. In order to obtain asingle value of distances, the distances of each axis are combined byusing a weighted average in which each signal is modulated by thecompactness of the clusters it generates.

FIGS. 8A-8C illustrate the use of a ‘best’ feature. In FIGS. 8A-8C, theresulting distance using the best axis (i.e., most compact clusters) areshown. FIGS. 9A-9C show the distance using the worst axis (i.e., mostspread clusters). The results shown in FIGS. 7A-7C (i.e., all the axis)indicate a better authentication of the user than the ones shown inFIGS. 8A-8C and 9A-9C. When using all the axis, the differences amongusers may be clearer making it easier to reject a user having higherdistances in this particular example. Additional data may aid furtherdistinguish this approach. FIGS. 9A-9C illustrate a method of approachusing the ‘worst’ feature.

Also described herein are methods and apparatuses including the use ofsupport vector data description. The Support Vector Data description(SVDD) deals with the problem of making a description of a trainingdataset with the aim of detecting which (new) data observations resemblethis training set. This procedure is also known as one-classclassification. Data description can be used for outlier detection, thatis, to detect uncharacteristic data values from a data set. In manyone-class classification problems there is a major complication, namelythat it is beforehand not clear what the specific distribution of thedata will be in practice. With SVDD, we obtain a spherically shapedboundary around the training dataset. We used SVDD to obtain thoseboundaries in the frequency domain of the accelerometer data, and thencomputed a confidence of being part of the training data. The plotsbelow (FIGS. 10A-10F) show the confidence level (e.g., the bars) fordifferent users using different garments. The highest bar corresponds tothe training data, thus we expect that the second highest bar alsocorresponds to the same user, when wearing a different garment, which isthe case for users MAMA, OSDA and RIRU.

FIGS. 10A-10F illustrate detection confidence for users MAMA, OSDA, andRIRU. The top ranking pair (user garment) corresponds always to thedataset that was used for training the model. We observed that the nexthigh confidence results correspond to the same user.

FIGS. 11A-11F shows the detection confidence for users EDPI, FRCA, andCODO. The top ranking pair (user garment) corresponds always to thedataset that was used for training the model. We observe that bothdatasets corresponding to user EDPI are subject to overfitting as themodel is not able to recognize the user wearing a different garment. Onthe other hand, the model corresponding to user CODO seems to be subjectto under-fitting as most of the other users display a high detectionconfidence as well. In general, FIGS. 11A-11F show detection confidencefor users EDPI, FRCA, and CODO.

Interestingly, we observed that the quality of the results in terms ofprediction accuracy for both the positive class (the target user) andthe negative class (all other users) does not depend on the amount ofsensor considered. Indeed, the difference in accuracy with respect tothe results presented above stays in the ballpark of +/−5% if weconsider the signal of any individual sensor instead of all combined.Nevertheless, we suspect that this might not be the case if we were torepeat this experiment on a larger set of users. In this case, theprobability of having similar signals among individuals would increasethus making the definition of unique user domains more difficult. Withmore sensors however, we are able to work in a higher dimensional spacewhere overlaps are less likely and identification is therefore improved.

Although the examples descried herein use Dynamic Time Warping insteadof Euclidean Distance, in some variations it may be more appropriategiven that out-of-phase time series can match prototype time-seriescharacterizing the authenticated user. For the second approach, the useof wavelet transforms instead of FFT may add time dependency to themodels and may be useful.

In general, further tests including other sensors and a combination ofmodel predictions (e.g., by using a Bayesian approach). The use of alarger collection of data for more accurate models may also be used.Testing the robustness and accuracy (e.g., test if a user can imitatethe behavior of another one) of the model. Other kind of features may beused to characterize the signals being used to authenticate the user.For instance, based on theoretical-information measures indicatingdisorder (entropy), complexity, fractal dimension and chaos dimensionmay be used.

As illustrated, it is possible to build user-specific models of behaviorfrom the available data, which indicates that authentication is feasiblebased on behavioral biometric data. Authentication is possible amongthis reduced group of people using all the IMU sensors in the garment.

This proof-of-concept is based on approaches using only one modality(accelerometer). This approach may be extended to a larger group orusers, using multiple modalities and combining multiple machinelearning-based authentication algorithms working in parallel.

When a feature or element is herein referred to as being “on” anotherfeature or element, it can be directly on the other feature or elementor intervening features and/or elements may also be present. Incontrast, when a feature or element is referred to as being “directlyon” another feature or element, there are no intervening features orelements present. It will also be understood that, when a feature orelement is referred to as being “connected”, “attached” or “coupled” toanother feature or element, it can be directly connected, attached orcoupled to the other feature or element or intervening features orelements may be present. In contrast, when a feature or element isreferred to as being “directly connected”, “directly attached” or“directly coupled” to another feature or element, there are nointervening features or elements present. Although described or shownwith respect to one embodiment, the features and elements so describedor shown can apply to other embodiments. It will also be appreciated bythose of skill in the art that references to a structure or feature thatis disposed “adjacent” another feature may have portions that overlap orunderlie the adjacent feature.

Terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention.For example, as used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, steps, operations, elements, components, and/orgroups thereof. As used herein, the term “and/or” includes any and allcombinations of one or more of the associated listed items and may beabbreviated as “/”.

Spatially relative terms, such as “under”, “below”, “lower”, “over”,“upper” and the like, may be used herein for ease of description todescribe one element or feature's relationship to another element(s) orfeature(s) as illustrated in the figures. It will be understood that thespatially relative terms are intended to encompass differentorientations of the device in use or operation in addition to theorientation depicted in the figures. For example, if a device in thefigures is inverted, elements described as “under” or “beneath” otherelements or features would then be oriented “over” the other elements orfeatures. Thus, the exemplary term “under” can encompass both anorientation of over and under. The device may be otherwise oriented(rotated 90 degrees or at other orientations) and the spatially relativedescriptors used herein interpreted accordingly. Similarly, the terms“upwardly”, “downwardly”, “vertical”, “horizontal” and the like are usedherein for the purpose of explanation only unless specifically indicatedotherwise.

Although the terms “first” and “second” may be used herein to describevarious features/elements (including steps), these features/elementsshould not be limited by these terms, unless the context indicatesotherwise. These terms may be used to distinguish one feature/elementfrom another feature/element. Thus, a first feature/element discussedbelow could be termed a second feature/element, and similarly, a secondfeature/element discussed below could be termed a first feature/elementwithout departing from the teachings of the present invention.

Throughout this specification and the claims which follow, unless thecontext requires otherwise, the word “comprise”, and variations such as“comprises” and “comprising” means various components can be co-jointlyemployed in the methods and articles (e.g., compositions and apparatusesincluding device and methods). For example, the term “comprising” willbe understood to imply the inclusion of any stated elements or steps butnot the exclusion of any other elements or steps.

As used herein in the specification and claims, including as used in theexamples and unless otherwise expressly specified, all numbers may beread as if prefaced by the word “about” or “approximately,” even if theterm does not expressly appear. The phrase “about” or “approximately”may be used when describing magnitude and/or position to indicate thatthe value and/or position described is within a reasonable expectedrange of values and/or positions. For example, a numeric value may havea value that is +/−0.1% of the stated value (or range of values), +/−1%of the stated value (or range of values), +/−2% of the stated value (orrange of values), +/−5% of the stated value (or range of values), +/−10%of the stated value (or range of values), etc. Any numerical valuesgiven herein should also be understood to include about or approximatelythat value, unless the context indicates otherwise. For example, if thevalue “10” is disclosed, then “about 10” is also disclosed. Anynumerical range recited herein is intended to include all sub-rangessubsumed therein. It is also understood that when a value is disclosedthat “less than or equal to” the value, “greater than or equal to thevalue” and possible ranges between values are also disclosed, asappropriately understood by the skilled artisan. For example, if thevalue “X” is disclosed the “less than or equal to X” as well as “greaterthan or equal to X” (e.g., where X is a numerical value) is alsodisclosed. It is also understood that the throughout the application,data is provided in a number of different formats, and that this data,represents endpoints and starting points, and ranges for any combinationof the data points. For example, if a particular data point “10” and aparticular data point “15” are disclosed, it is understood that greaterthan, greater than or equal to, less than, less than or equal to, andequal to 10 and 15 are considered disclosed as well as between 10 and15. It is also understood that each unit between two particular unitsare also disclosed. For example, if 10 and 15 are disclosed, then 11,12, 13, and 14 are also disclosed.

Although various illustrative embodiments are described above, any of anumber of changes may be made to various embodiments without departingfrom the scope of the invention as described by the claims. For example,the order in which various described method steps are performed mayoften be changed in alternative embodiments, and in other alternativeembodiments one or more method steps may be skipped altogether. Optionalfeatures of various device and system embodiments may be included insome embodiments and not in others. Therefore, the foregoing descriptionis provided primarily for exemplary purposes and should not beinterpreted to limit the scope of the invention as it is set forth inthe claims.

The examples and illustrations included herein show, by way ofillustration and not of limitation, specific embodiments in which thesubject matter may be practiced. As mentioned, other embodiments may beutilized and derived there from, such that structural and logicalsubstitutions and changes may be made without departing from the scopeof this disclosure. Such embodiments of the inventive subject matter maybe referred to herein individually or collectively by the term“invention” merely for convenience and without intending to voluntarilylimit the scope of this application to any single invention or inventiveconcept, if more than one is, in fact, disclosed. Thus, althoughspecific embodiments have been illustrated and described herein, anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

What is claimed is:
 1. A method of confirming a user's identity, themethod comprising: wearing a garment comprising a plurality ofintegrated sensors at predetermined locations; synchronously recordingsensor data from multiple predetermined locations on the garment;generating, in the garment, a biometric token from the recorded sensordata; transmitting the biometric token to a lodger in or on the garment;and transmitting the biometric token to a third party having a biometrictemplate against which the biometric token may be tested.
 2. The methodof claim 1, wherein generating a biometric token from the recordedsensor data comprises generating the biometric token in a scheduler onthe garment.
 3. The method of claim 1, wherein wearing the garmentcomprises adjusting the position of the sensors based on haptic feedbackfrom the garment.
 4. The method of claim 1, wherein synchronouslyrecording the sensor data comprises synchronously recording the sensordata from a plurality of motion sensors.
 5. The method of claim 1,wherein synchronously recording the sensor data comprises synchronouslyrecording the sensor data from a plurality of motion sensors, one ormore respiration sensors and one or more electrodes configured tocontact the user's skin when the garment is worn.
 6. The method of claim1, wherein wearing the garment comprises wearing the garment over theuser's torso.
 7. The method of claim 1, wherein synchronously recordingthe sensor data comprises synchronously recording the sensor data frommultiple sensor types on the garment.
 8. The method of claim 7, whereinsynchronously recording the sensor data comprises recording the data ata plurality of frequencies.
 9. The method of claim 1, further comprisingencrypting the biometric token prior to transmitting the biometric tokento the third party.
 10. The method of claim 1, further comprisingsending a coded message requesting approval of the user to proceed fromthe third party.
 11. The method of claim 10, further comprisingcontacting an output on the garment to indicate agreement to the thirdparty.
 12. A method of confirming a user's identity, the methodcomprising: wearing a garment comprising a plurality of integratedsensors at predetermined locations in the garment that are configured toposition the integrated sensors over the user's torso; synchronouslyrecording sensor data from multiple predetermined locations on thegarment, using a plurality of different sensor types; generating, in thegarment, a biometric token from the recorded sensor data; andtransmitting the biometric token to a third party having a biometrictemplate against which the biometric token may be tested.
 13. The methodof claim 12, wherein generating the biometric token from the recordedsensor data comprises generating the biometric token in a scheduler onthe garment.
 14. The method of claim 12, wherein wearing the garmentcomprises adjusting the position of the sensors based on haptic feedbackfrom the garment.
 15. The method of claim 12, wherein synchronouslyrecording the sensor data comprises synchronously recording the sensordata from a plurality of motion sensors.
 16. The method of claim 12,wherein synchronously recording the sensor data comprises synchronouslyrecording the sensor data from a plurality of motion sensors, one ormore respiration sensors and one or more electrodes configured tocontact the user's skin when the garment is worn.
 17. The method ofclaim 12, wherein synchronously recording the sensor data comprisesrecording the sensor data at a plurality of frequencies.
 18. The methodof claim 12, further comprising encrypting the biometric token prior totransmitting the biometric token to the third party.
 19. The method ofclaim 12, further comprising sending a coded message requesting approvalof the user to proceed from the third party.
 20. The method of claim 19,further comprising contacting an output on the garment to indicateagreement to the third party.
 21. A method of confirming a user'sidentity, the method comprising: wearing a garment comprising aplurality of integrated sensors at predetermined locations in thegarment that are configured to position the integrated sensors over theuser's torso; adjusting the position of the sensors using hapticfeedback from the garment; synchronously recording sensor data frommultiple predetermined locations on the garment, using a plurality ofdifferent sensor types; generating, in the garment, a biometric tokenfrom the recorded sensor data; encrypting the biometric token; andtransmitting the encrypted biometric token to a third party having abiometric template against which the biometric token may be tested.